One of the major problems that may occur in the differential protection systems of power transformers is mal-operation of the protection relays in sake of internal fault detection, because of similarity between this current and inrush current. This paper presents a novel approach for discriminating inrush current from internal fault in power transformers based on Improved Gravitational Search Algorithm (IGSA). For this purpose, an Artificial Neural Network (ANN) which is trained by IGSA has been applied to discrete sample data of internal fault and inrush currents in the transformers. Results show that, the used approach can discriminate between these two kinds of phenomenon, very well and also, has high accuracy and excellent reliability, in addition, it has less computational burden and complexity. پرونده مقاله

Extension of inter-turn fault in windings of PMSM can damage all parts of electrical systems, and in some cases in sensitive applications may lead to irreparable events. Identification of such small faults at incipient steps can be so helpful to protect entire part of electrical system. In this paper, intelligent protection system is designed which is made by two major parts. In the first part of intelligent protection system K-Nearest Neighbor classifier is used as a detecting system to discriminate inter-turn fault from normal condition, phase to phase fault and open circuit condition and also to detect faulty phase, simultaneity. After that if inter-turn fault is happened, second part of proposed system which is based on an ANN Trained with Improved Gravitational Search Algorithm determines the amount of fault. IGSA is presented to improve the performance of the proposed protection system in this paper. Obtained results show that both part of intelligent proposed and intelligent protection system can do their best performance. It can successfully detect inter-turn fault and follow it and predict amount of this fault. پرونده مقاله